免疫多域特征融合的多核学习SVM运动想象脑电信号分类  

Motor Imagery EEG Classification Based on Immune Multi-domain-feature Fusion and Multiple Kernel Learning SVM

作　　者：张宪法 郝矿荣[1,2] 陈磊 ZHANG Xian-Fa;HAO Kuang-Rong;CHEN Lei(School of Information Science and Technology,Donghua Uni-versity,Shanghai 201620;Engineering Research Center of Digitized Textile&Apparel Technology,Ministry of Education)

机构地区：[1]东华大学信息科学与技术学院,上海201620 [2]东华大学数字化纺织服装技术教育部工程研究中心,上海201620

出　　处：《自动化学报》2020年第11期2417-2426,共10页Acta Automatica Sinica

基　　金：国家自然科学基金(61806051,61903078);上海市扬帆计划(17YF1426100);上海市自然科学基金(19ZR1402300,20ZR1400400)资助。

摘　　要：针对多通道四类运动想象(Motor imagery,MI)脑电信号(Electroencephalography,EEG)的分类问题,提出免疫多域特征融合的多核学习SVM(Support vector machine)运动想象脑电信号分类算法.首先,通过离散小波变换(Discrete wavelet transform,DWT)提取脑电信号的时频域特征,并利用一对多公共空间模式(One versus the rest common spatial patterns,OVR-CSP)提取脑电信号的空域特征,融合时频空域特征形成特征向量.其次,利用多核学习支持向量机(Multiple kernel learning support vector machine,MKL-SVM)对提取的特征向量进行分类.最后,利用免疫遗传算法(Immune genetic algorithm,IGA)对模型的相关参数进行优化,得到识别率更高的脑电信号分类模型.采用BCI2005desc-Ⅲa数据集进行实验验证,对比结果表明,本文所提出的分类模型有效地解决了传统单域特征提取算法特征单一、信息描述不足的问题,更准确地表达了不同受试者个性化的多域特征,取得了94.21%的识别率,优于使用相同数据集的其他方法.For the classification problem of four kinds of multi-channel motor imagery(MI)EEG,a multi-class motor imagery EEG classification algorithm based on immune multi-domain-feature fusion and multiple kernel learning SVM is proposed.Firstly,discrete wavelet transform(DWT)is used to extract the time-frequency features of EEG signals.The spatial features of EEG are extracted by using one versus the rest common spatial patterns(OVR-CSP).And then time-frequency and spatial features are fused to form one-dimensional feature vector.Secondly,multiple kernel learning support vector machine(MKL-SVM)is used as a classifier to classify the feature vectors.Finally,immune genetic algorithm(IGA)is used to optimize the parameters of MKL-SVM.The experimental results on BCI2005desc-Ⅲa dataset show that the classification model proposed in this paper not only effectively overcomes the shortcomings that is the traditional single-domain feature extraction algorithm is lack of information description,but also more accurately expresses the multi-domain characteristics of different subjects.It achieves a recognition rate of 94.21%,which is better than other methods of using the same data set.

关 键 词：离散小波变换 公共空间模式 多核学习支持向量机 免疫遗传 运动想象 脑电信号 

分 类 号：TN911.7[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程] R318[自动化与计算机技术—控制理论与控制工程]